In [13]:
import matplotlib.pyplot as plt
import networkx as nx
from pprint import pprint
country_graph = nx.from_pandas_edgelist(df=country_pairs_norm[country_pairs_norm['cites_norm_gdppc_sum']>1],
source='node1', target='node2', edge_attr='cites_norm_gdppc_sum')
nx.set_node_attributes(country_graph, country_node_attrs)
nx.write_gexf(country_graph, f"gephi/country_graph-{target_concept}-{n_samples}.gexf")
# Set the background color to white
plt.rcParams['axes.facecolor'] = 'white'
# Expand the figure size for better visibility
plt.figure(figsize=(10, 8))
# Draw the graph with node sizes and red edges
pos = nx.spring_layout(country_graph, seed=42, k=10, iterations=50)
labels = {e: round(country_graph.edges[e]['cites_norm_gdppc_sum'],3) for e in country_graph.edges}
color_code = {
'High income': 'blue',
'Upper middle income': 'green',
'Lower middle income': 'yellow',
'Low income': 'red'
}
node_color = [ 'gray' if c is None else c for c in
[ color_code.get(country_graph.nodes[node]['Income group']) for node in country_graph.nodes() ]
]
node_size = [ country_graph.nodes[node]['avg_cites_per_coauthor'] * 50 for node in country_graph.nodes() ]
# pprint(labels)
# pprint(country_node_attrs)
nx.draw(country_graph, pos, node_size=node_size, node_color=node_color, with_labels=True)
nx.draw_networkx_edge_labels(country_graph, pos, edge_labels=labels, label_pos=0.5,
horizontalalignment='right', verticalalignment='top')
# Show the graph
plt.show()
